Overview

Brought to you by YData

Dataset statistics

Number of variables52
Number of observations484
Missing cells966
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory570.5 KiB
Average record size in memory1.2 KiB

Variable types

Numeric8
Text9
Categorical34
Unsupported1

Alerts

isFournisseur has constant value "0" Constant
Note has constant value "0" Constant
Type has constant value "0" Constant
FournitMP has constant value "0" Constant
FournitMB has constant value "0" Constant
NumInterne has constant value "0" Constant
TauxRetenueSource has constant value "0.0" Constant
ExonerationRS has constant value "0" Constant
IsPDR has constant value "0" Constant
Timbre has constant value "0" Constant
ToleranceMAxAccepte has constant value "0.0" Constant
IDBanque has constant value "0" Constant
AdresseBanque has constant value "" Constant
VilleBanque has constant value "" Constant
NumCompte has constant value "" Constant
CodeSwift has constant value "" Constant
IBAN has constant value "" Constant
NonAssujettiTVA has constant value "0" Constant
DelaisLivraison has constant value "0" Constant
Reference has constant value "" Constant
IDFournisseurParent has constant value "0" Constant
Difference has constant value "0.0" Constant
AppliqueFodec has constant value "0" Constant
Login_FRS has constant value "" Constant
IDPlanComptable has constant value "0" Constant
Chiffre is highly overall correlated with Reglements and 1 other fieldsHigh correlation
Etat is highly overall correlated with FournitPF and 6 other fieldsHigh correlation
FournitPF is highly overall correlated with Etat and 6 other fieldsHigh correlation
IDCGAFournisseur is highly overall correlated with IDPaysHigh correlation
IDCategorie is highly overall correlated with IDPays and 2 other fieldsHigh correlation
IDConditionReglement is highly overall correlated with IDModeReglementHigh correlation
IDDevise is highly overall correlated with Etat and 4 other fieldsHigh correlation
IDFournisseur is highly overall correlated with Etat and 5 other fieldsHigh correlation
IDModeReglement is highly overall correlated with Etat and 5 other fieldsHigh correlation
IDPays is highly overall correlated with Etat and 5 other fieldsHigh correlation
IsPF is highly overall correlated with IDFournisseurHigh correlation
Pays is highly overall correlated with Etat and 3 other fieldsHigh correlation
Reglements is highly overall correlated with Chiffre and 2 other fieldsHigh correlation
Solde is highly overall correlated with ChiffreHigh correlation
isService is highly overall correlated with Etat and 6 other fieldsHigh correlation
Pays is highly imbalanced (74.7%) Imbalance
IDModeReglement is highly imbalanced (68.2%) Imbalance
IDCGAFournisseur is highly imbalanced (81.2%) Imbalance
IsMP is highly imbalanced (97.9%) Imbalance
Observations has 482 (99.6%) missing values Missing
DateExonerationRS has 484 (100.0%) missing values Missing
IDFournisseur has unique values Unique
Code has unique values Unique
DateExonerationRS is an unsupported type, check if it needs cleaning or further analysis Unsupported
Chiffre has 127 (26.2%) zeros Zeros
Reglements has 190 (39.3%) zeros Zeros
Solde has 261 (53.9%) zeros Zeros
IDCategorie has 73 (15.1%) zeros Zeros
IDPays has 132 (27.3%) zeros Zeros
Echeance has 473 (97.7%) zeros Zeros
IDConditionReglement has 401 (82.9%) zeros Zeros

Reproduction

Analysis started2025-03-09 14:52:48.293764
Analysis finished2025-03-09 14:53:05.602065
Duration17.31 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

IDFournisseur
Real number (ℝ)

High correlation  Unique 

Distinct484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.94835
Minimum6
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:05.775234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile105.3
Q1327.75
median456
Q3584.25
95-th percentile684.85
Maximum713
Range707
Interquartile range (IQR)256.5

Descriptive statistics

Standard deviation174.30762
Coefficient of variation (CV)0.39530167
Kurtosis-0.54024711
Mean440.94835
Median Absolute Deviation (MAD)128.5
Skewness-0.47851212
Sum213419
Variance30383.146
MonotonicityStrictly increasing
2025-03-09T15:53:06.012865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713 1
 
0.2%
6 1
 
0.2%
19 1
 
0.2%
25 1
 
0.2%
696 1
 
0.2%
692 1
 
0.2%
691 1
 
0.2%
690 1
 
0.2%
689 1
 
0.2%
688 1
 
0.2%
Other values (474) 474
97.9%
ValueCountFrequency (%)
6 1
0.2%
19 1
0.2%
25 1
0.2%
29 1
0.2%
32 1
0.2%
33 1
0.2%
38 1
0.2%
39 1
0.2%
42 1
0.2%
46 1
0.2%
ValueCountFrequency (%)
713 1
0.2%
712 1
0.2%
711 1
0.2%
710 1
0.2%
709 1
0.2%
708 1
0.2%
707 1
0.2%
706 1
0.2%
705 1
0.2%
704 1
0.2%

Fournisseur
Text

Distinct482
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size32.1 KiB
2025-03-09T15:53:06.409570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length36
Mean length18.727273
Min length0

Characters and Unicode

Total characters9064
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)99.2%

Sample

1st rowBALINLER PAZARLAMA VE TICARET
2nd rowDRISHTI APPARELS
3rd rowTRUMODE INTERNATIONAL LTD.
4th rowSHANGHAI SILK GROUP TRADING DEVELOPMENT CO., LTD
5th rowGULEKS TEKSTIL SAN TIC LTD STI
ValueCountFrequency (%)
naf 53
 
3.7%
ltd 27
 
1.9%
sas 23
 
1.6%
de 20
 
1.4%
sci 17
 
1.2%
co 16
 
1.1%
france 14
 
1.0%
tekstil 13
 
0.9%
san 13
 
0.9%
cc 13
 
0.9%
Other values (900) 1237
85.5%
2025-03-09T15:53:07.005814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1029
 
11.4%
A 900
 
9.9%
E 836
 
9.2%
S 638
 
7.0%
N 624
 
6.9%
I 617
 
6.8%
T 509
 
5.6%
R 463
 
5.1%
L 407
 
4.5%
O 403
 
4.4%
Other values (56) 2638
29.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7667
84.6%
Space Separator 1029
 
11.4%
Decimal Number 131
 
1.4%
Other Punctuation 102
 
1.1%
Dash Punctuation 60
 
0.7%
Lowercase Letter 60
 
0.7%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 900
11.7%
E 836
10.9%
S 638
 
8.3%
N 624
 
8.1%
I 617
 
8.0%
T 509
 
6.6%
R 463
 
6.0%
L 407
 
5.3%
O 403
 
5.3%
C 376
 
4.9%
Other values (16) 1894
24.7%
Lowercase Letter
ValueCountFrequency (%)
a 10
16.7%
o 7
11.7%
n 6
10.0%
u 5
8.3%
t 4
 
6.7%
i 4
 
6.7%
s 3
 
5.0%
r 3
 
5.0%
e 3
 
5.0%
l 3
 
5.0%
Other values (9) 12
20.0%
Decimal Number
ValueCountFrequency (%)
2 20
15.3%
5 16
12.2%
3 16
12.2%
8 15
11.5%
4 13
9.9%
6 13
9.9%
1 12
9.2%
9 11
8.4%
7 10
7.6%
0 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 62
60.8%
' 25
24.5%
& 6
 
5.9%
, 5
 
4.9%
/ 3
 
2.9%
: 1
 
1.0%
Space Separator
ValueCountFrequency (%)
1029
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7727
85.2%
Common 1337
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 900
11.6%
E 836
10.8%
S 638
 
8.3%
N 624
 
8.1%
I 617
 
8.0%
T 509
 
6.6%
R 463
 
6.0%
L 407
 
5.3%
O 403
 
5.2%
C 376
 
4.9%
Other values (35) 1954
25.3%
Common
ValueCountFrequency (%)
1029
77.0%
. 62
 
4.6%
- 60
 
4.5%
' 25
 
1.9%
2 20
 
1.5%
5 16
 
1.2%
3 16
 
1.2%
8 15
 
1.1%
4 13
 
1.0%
6 13
 
1.0%
Other values (11) 68
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1029
 
11.4%
A 900
 
9.9%
E 836
 
9.2%
S 638
 
7.0%
N 624
 
6.9%
I 617
 
6.8%
T 509
 
5.6%
R 463
 
5.1%
L 407
 
4.5%
O 403
 
4.4%
Other values (56) 2638
29.1%

Adresse
Text

Distinct408
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
2025-03-09T15:53:07.482279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length150
Median length70
Mean length35.328512
Min length0

Characters and Unicode

Total characters17099
Distinct characters93
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391 ?
Unique (%)80.8%

Sample

1st rowGULTEPE MAH TURE SOK N°12
2nd rowPLOT NO 180 SECTOR -6 IM MANES
3rd row3-5F N0 9. LOFT POWER N0 28 XI
4th rowN°283 WUXING ROAD
5th rowAMBARLI PETROL OFSI DOLUM
ValueCountFrequency (%)
rue 155
 
5.3%
de 93
 
3.2%
paris 90
 
3.1%
83
 
2.8%
du 49
 
1.7%
la 47
 
1.6%
avenue 44
 
1.5%
des 41
 
1.4%
cedex 38
 
1.3%
cs 30
 
1.0%
Other values (1383) 2262
77.1%
2025-03-09T15:53:08.259826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2503
 
14.6%
E 1039
 
6.1%
A 828
 
4.8%
R 687
 
4.0%
0 652
 
3.8%
I 570
 
3.3%
N 540
 
3.2%
e 522
 
3.1%
S 510
 
3.0%
L 497
 
2.9%
Other values (83) 8751
51.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8043
47.0%
Lowercase Letter 3084
 
18.0%
Decimal Number 2722
 
15.9%
Space Separator 2503
 
14.6%
Other Punctuation 346
 
2.0%
Control 203
 
1.2%
Dash Punctuation 172
 
1.0%
Other Symbol 13
 
0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 522
16.9%
a 291
9.4%
r 248
 
8.0%
n 244
 
7.9%
u 243
 
7.9%
i 223
 
7.2%
o 180
 
5.8%
s 178
 
5.8%
d 163
 
5.3%
l 155
 
5.0%
Other values (22) 637
20.7%
Uppercase Letter
ValueCountFrequency (%)
E 1039
12.9%
A 828
 
10.3%
R 687
 
8.5%
I 570
 
7.1%
N 540
 
6.7%
S 510
 
6.3%
L 497
 
6.2%
U 440
 
5.5%
C 376
 
4.7%
O 368
 
4.6%
Other values (21) 2188
27.2%
Decimal Number
ValueCountFrequency (%)
0 652
24.0%
1 374
13.7%
2 301
11.1%
7 266
9.8%
5 258
 
9.5%
3 219
 
8.0%
6 180
 
6.6%
9 180
 
6.6%
4 174
 
6.4%
8 118
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 218
63.0%
. 53
 
15.3%
' 33
 
9.5%
/ 26
 
7.5%
: 11
 
3.2%
? 2
 
0.6%
" 2
 
0.6%
# 1
 
0.3%
Control
ValueCountFrequency (%)
104
51.2%
98
48.3%
1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 169
98.3%
– 3
 
1.7%
Space Separator
ValueCountFrequency (%)
2503
100.0%
Other Symbol
ValueCountFrequency (%)
° 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11127
65.1%
Common 5972
34.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1039
 
9.3%
A 828
 
7.4%
R 687
 
6.2%
I 570
 
5.1%
N 540
 
4.9%
e 522
 
4.7%
S 510
 
4.6%
L 497
 
4.5%
U 440
 
4.0%
C 376
 
3.4%
Other values (53) 5118
46.0%
Common
ValueCountFrequency (%)
2503
41.9%
0 652
 
10.9%
1 374
 
6.3%
2 301
 
5.0%
7 266
 
4.5%
5 258
 
4.3%
3 219
 
3.7%
, 218
 
3.7%
6 180
 
3.0%
9 180
 
3.0%
Other values (20) 821
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17023
99.6%
None 71
 
0.4%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2503
 
14.7%
E 1039
 
6.1%
A 828
 
4.9%
R 687
 
4.0%
0 652
 
3.8%
I 570
 
3.3%
N 540
 
3.2%
e 522
 
3.1%
S 510
 
3.0%
L 497
 
2.9%
Other values (68) 8675
51.0%
None
ValueCountFrequency (%)
é 32
45.1%
° 13
18.3%
â 4
 
5.6%
ç 3
 
4.2%
ö 3
 
4.2%
è 3
 
4.2%
 3
 
4.2%
ü 3
 
4.2%
Ü 3
 
4.2%
Ö 1
 
1.4%
Other values (3) 3
 
4.2%
Punctuation
ValueCountFrequency (%)
– 3
60.0%
’ 2
40.0%

Code
Text

Unique 

Distinct484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.8 KiB
2025-03-09T15:53:08.674787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length8
Mean length7.322314
Min length0

Characters and Unicode

Total characters3544
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)100.0%

Sample

1st rowFRM00615
2nd rowFRM01071
3rd rowFRM01410
4th rowFRM01603
5th rowGULEKS TEKSTIL
ValueCountFrequency (%)
mustafa 2
 
0.4%
ltd 2
 
0.4%
frm02949 1
 
0.2%
016 1
 
0.2%
015 1
 
0.2%
014 1
 
0.2%
013 1
 
0.2%
012 1
 
0.2%
010 1
 
0.2%
f0000136 1
 
0.2%
Other values (487) 487
97.6%
2025-03-09T15:53:09.371994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1403
39.6%
F 277
 
7.8%
1 189
 
5.3%
3 156
 
4.4%
M 153
 
4.3%
2 141
 
4.0%
O 123
 
3.5%
L 122
 
3.4%
R 117
 
3.3%
Y 115
 
3.2%
Other values (33) 748
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2347
66.2%
Uppercase Letter 1168
33.0%
Space Separator 16
 
0.5%
Lowercase Letter 12
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 277
23.7%
M 153
13.1%
O 123
10.5%
L 122
10.4%
R 117
10.0%
Y 115
9.8%
A 71
 
6.1%
E 33
 
2.8%
T 25
 
2.1%
I 22
 
1.9%
Other values (16) 110
 
9.4%
Decimal Number
ValueCountFrequency (%)
0 1403
59.8%
1 189
 
8.1%
3 156
 
6.6%
2 141
 
6.0%
4 89
 
3.8%
9 82
 
3.5%
8 77
 
3.3%
5 74
 
3.2%
6 69
 
2.9%
7 67
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
a 4
33.3%
t 2
16.7%
s 2
16.7%
u 2
16.7%
f 2
16.7%
Space Separator
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
66.7%
Latin 1180
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 277
23.5%
M 153
13.0%
O 123
10.4%
L 122
10.3%
R 117
9.9%
Y 115
9.7%
A 71
 
6.0%
E 33
 
2.8%
T 25
 
2.1%
I 22
 
1.9%
Other values (21) 122
10.3%
Common
ValueCountFrequency (%)
0 1403
59.3%
1 189
 
8.0%
3 156
 
6.6%
2 141
 
6.0%
4 89
 
3.8%
9 82
 
3.5%
8 77
 
3.3%
5 74
 
3.1%
6 69
 
2.9%
7 67
 
2.8%
Other values (2) 17
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1403
39.6%
F 277
 
7.8%
1 189
 
5.3%
3 156
 
4.4%
M 153
 
4.3%
2 141
 
4.0%
O 123
 
3.5%
L 122
 
3.4%
R 117
 
3.3%
Y 115
 
3.2%
Other values (33) 748
21.1%

Email
Text

Distinct189
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2025-03-09T15:53:09.782087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length0
Mean length9.9586777
Min length0

Characters and Unicode

Total characters4820
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)37.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
gestionlocative@nhood.com 3
 
1.5%
etardy@altarea.com 2
 
1.0%
gl_contact_client@klepierre.com 2
 
1.0%
shaidy@mercialys.com 2
 
1.0%
guillaume.bourdet@groupe-elancia.fr 2
 
1.0%
nsorand@galimmo.com 2
 
1.0%
sindy.klock@espace-expansion.fr 2
 
1.0%
gaelle.dendele@espace-expansion.fr 2
 
1.0%
sunshine@nahuli.com 2
 
1.0%
jauffret@mercialys.com 2
 
1.0%
Other values (177) 177
89.4%
2025-03-09T15:53:10.411136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 404
 
8.4%
a 396
 
8.2%
o 363
 
7.5%
i 340
 
7.1%
c 321
 
6.7%
r 310
 
6.4%
t 274
 
5.7%
. 269
 
5.6%
n 262
 
5.4%
m 260
 
5.4%
Other values (48) 1621
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4166
86.4%
Other Punctuation 465
 
9.6%
Uppercase Letter 87
 
1.8%
Decimal Number 51
 
1.1%
Dash Punctuation 42
 
0.9%
Connector Punctuation 8
 
0.2%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 404
9.7%
a 396
 
9.5%
o 363
 
8.7%
i 340
 
8.2%
c 321
 
7.7%
r 310
 
7.4%
t 274
 
6.6%
n 262
 
6.3%
m 260
 
6.2%
l 233
 
5.6%
Other values (16) 1003
24.1%
Uppercase Letter
ValueCountFrequency (%)
C 13
14.9%
T 11
12.6%
I 8
9.2%
L 7
8.0%
A 7
8.0%
O 7
8.0%
N 6
6.9%
E 6
6.9%
S 5
 
5.7%
G 4
 
4.6%
Other values (8) 13
14.9%
Decimal Number
ValueCountFrequency (%)
2 10
19.6%
0 10
19.6%
1 8
15.7%
6 7
13.7%
9 6
11.8%
5 3
 
5.9%
7 3
 
5.9%
3 2
 
3.9%
8 2
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 269
57.8%
@ 196
42.2%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4253
88.2%
Common 567
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 404
 
9.5%
a 396
 
9.3%
o 363
 
8.5%
i 340
 
8.0%
c 321
 
7.5%
r 310
 
7.3%
t 274
 
6.4%
n 262
 
6.2%
m 260
 
6.1%
l 233
 
5.5%
Other values (34) 1090
25.6%
Common
ValueCountFrequency (%)
. 269
47.4%
@ 196
34.6%
- 42
 
7.4%
2 10
 
1.8%
0 10
 
1.8%
_ 8
 
1.4%
1 8
 
1.4%
6 7
 
1.2%
9 6
 
1.1%
5 3
 
0.5%
Other values (4) 8
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 404
 
8.4%
a 396
 
8.2%
o 363
 
7.5%
i 340
 
7.1%
c 321
 
6.7%
r 310
 
6.4%
t 274
 
5.7%
. 269
 
5.6%
n 262
 
5.4%
m 260
 
5.4%
Other values (48) 1621
33.6%

Tel
Text

Distinct254
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Memory size26.8 KiB
2025-03-09T15:53:10.791141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length19
Mean length7.3780992
Min length0

Characters and Unicode

Total characters3571
Distinct characters24
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique244 ?
Unique (%)50.4%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
33 33
 
3.9%
01 29
 
3.4%
03 26
 
3.1%
04 26
 
3.1%
00 23
 
2.7%
02 15
 
1.8%
06 14
 
1.6%
70 14
 
1.6%
55 13
 
1.5%
72 12
 
1.4%
Other values (261) 646
75.9%
2025-03-09T15:53:11.353579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
590
16.5%
0 553
15.5%
3 344
9.6%
4 283
7.9%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.4%
5 225
 
6.3%
8 211
 
5.9%
Other values (14) 346
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2809
78.7%
Space Separator 590
 
16.5%
Other Punctuation 69
 
1.9%
Math Symbol 45
 
1.3%
Open Punctuation 18
 
0.5%
Close Punctuation 18
 
0.5%
Dash Punctuation 14
 
0.4%
Lowercase Letter 8
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 553
19.7%
3 344
12.2%
4 283
10.1%
1 277
9.9%
7 263
9.4%
2 249
8.9%
6 230
8.2%
5 225
8.0%
8 211
 
7.5%
9 174
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
m 2
25.0%
g 1
12.5%
a 1
12.5%
i 1
12.5%
l 1
12.5%
c 1
12.5%
o 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 67
97.1%
/ 2
 
2.9%
Space Separator
ValueCountFrequency (%)
590
100.0%
Math Symbol
ValueCountFrequency (%)
+ 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3563
99.8%
Latin 8
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
590
16.6%
0 553
15.5%
3 344
9.7%
4 283
7.9%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.5%
5 225
 
6.3%
8 211
 
5.9%
Other values (7) 338
9.5%
Latin
ValueCountFrequency (%)
m 2
25.0%
g 1
12.5%
a 1
12.5%
i 1
12.5%
l 1
12.5%
c 1
12.5%
o 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
590
16.5%
0 553
15.5%
3 344
9.6%
4 283
7.9%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.4%
5 225
 
6.3%
8 211
 
5.9%
Other values (14) 346
9.7%

Fax
Text

Distinct63
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2025-03-09T15:53:11.627036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length0
Mean length1.731405
Min length0

Characters and Unicode

Total characters838
Distinct characters31
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)12.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
01 11
 
5.7%
04 10
 
5.2%
33 9
 
4.7%
03 7
 
3.6%
02 5
 
2.6%
72 4
 
2.1%
08 4
 
2.1%
67 4
 
2.1%
09 3
 
1.6%
05 3
 
1.6%
Other values (101) 133
68.9%
2025-03-09T15:53:12.110730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
15.9%
0 110
13.1%
2 70
8.4%
4 70
8.4%
3 70
8.4%
1 62
7.4%
7 62
7.4%
8 51
 
6.1%
9 49
 
5.8%
6 49
 
5.8%
Other values (21) 112
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 640
76.4%
Space Separator 133
 
15.9%
Lowercase Letter 25
 
3.0%
Other Punctuation 24
 
2.9%
Math Symbol 5
 
0.6%
Dash Punctuation 5
 
0.6%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4
16.0%
l 4
16.0%
e 3
12.0%
m 2
8.0%
o 2
8.0%
i 2
8.0%
s 1
 
4.0%
b 1
 
4.0%
h 1
 
4.0%
z 1
 
4.0%
Other values (4) 4
16.0%
Decimal Number
ValueCountFrequency (%)
0 110
17.2%
2 70
10.9%
4 70
10.9%
3 70
10.9%
1 62
9.7%
7 62
9.7%
8 51
8.0%
9 49
7.7%
6 49
7.7%
5 47
7.3%
Other Punctuation
ValueCountFrequency (%)
. 23
95.8%
@ 1
 
4.2%
Space Separator
ValueCountFrequency (%)
133
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 813
97.0%
Latin 25
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
133
16.4%
0 110
13.5%
2 70
8.6%
4 70
8.6%
3 70
8.6%
1 62
7.6%
7 62
7.6%
8 51
 
6.3%
9 49
 
6.0%
6 49
 
6.0%
Other values (7) 87
10.7%
Latin
ValueCountFrequency (%)
a 4
16.0%
l 4
16.0%
e 3
12.0%
m 2
8.0%
o 2
8.0%
i 2
8.0%
s 1
 
4.0%
b 1
 
4.0%
h 1
 
4.0%
z 1
 
4.0%
Other values (4) 4
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
15.9%
0 110
13.1%
2 70
8.4%
4 70
8.4%
3 70
8.4%
1 62
7.4%
7 62
7.4%
8 51
 
6.1%
9 49
 
5.8%
6 49
 
5.8%
Other values (21) 112
13.4%

isFournisseur
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:12.280055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:12.368755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Chiffre
Real number (ℝ)

High correlation  Zeros 

Distinct355
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50956.009
Minimum0
Maximum1954887.1
Zeros127
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:12.528678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4008.991
Q330528.961
95-th percentile253883.06
Maximum1954887.1
Range1954887.1
Interquartile range (IQR)30528.961

Descriptive statistics

Standard deviation146493.2
Coefficient of variation (CV)2.8748955
Kurtosis71.940098
Mean50956.009
Median Absolute Deviation (MAD)4008.991
Skewness7.1365253
Sum24662708
Variance2.1460258 × 1010
MonotonicityNot monotonic
2025-03-09T15:53:12.820769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127
 
26.2%
372 2
 
0.4%
3000 2
 
0.4%
850 2
 
0.4%
480 1
 
0.2%
1508.388 1
 
0.2%
2230.56 1
 
0.2%
54 1
 
0.2%
1020 1
 
0.2%
925 1
 
0.2%
Other values (345) 345
71.3%
ValueCountFrequency (%)
0 127
26.2%
8.08 1
 
0.2%
18 1
 
0.2%
36 1
 
0.2%
48 1
 
0.2%
54 1
 
0.2%
56.51 1
 
0.2%
60 1
 
0.2%
91.44 1
 
0.2%
92.4 1
 
0.2%
ValueCountFrequency (%)
1954887.06 1
0.2%
1207569.416 1
0.2%
872721.06 1
0.2%
839037.014 1
0.2%
790056.016 1
0.2%
678859.682 1
0.2%
589731.67 1
0.2%
455731.964 1
0.2%
432111.4 1
0.2%
419750.18 1
0.2%

Reglements
Real number (ℝ)

High correlation  Zeros 

Distinct293
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31817.698
Minimum0
Maximum1420220.7
Zeros190
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:13.040885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median742.8
Q320575.517
95-th percentile147267.74
Maximum1420220.7
Range1420220.7
Interquartile range (IQR)20575.517

Descriptive statistics

Standard deviation96553.362
Coefficient of variation (CV)3.0345804
Kurtosis95.791923
Mean31817.698
Median Absolute Deviation (MAD)742.8
Skewness8.0955818
Sum15399766
Variance9.3225517 × 109
MonotonicityNot monotonic
2025-03-09T15:53:13.271598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 190
39.3%
372 2
 
0.4%
850 2
 
0.4%
6509.5 1
 
0.2%
12978.36 1
 
0.2%
18600 1
 
0.2%
2958 1
 
0.2%
9445.745 1
 
0.2%
115.2 1
 
0.2%
60 1
 
0.2%
Other values (283) 283
58.5%
ValueCountFrequency (%)
0 190
39.3%
8.08 1
 
0.2%
18 1
 
0.2%
36 1
 
0.2%
54 1
 
0.2%
60 1
 
0.2%
91.44 1
 
0.2%
92.4 1
 
0.2%
104.94 1
 
0.2%
106.32 1
 
0.2%
ValueCountFrequency (%)
1420220.68 1
0.2%
639357.77 1
0.2%
573078.5 1
0.2%
468342.732 1
0.2%
437510.4 1
0.2%
415477.68 1
0.2%
407082.763 1
0.2%
402241.67 1
0.2%
399852.864 1
0.2%
280392.5 1
0.2%

Solde
Real number (ℝ)

High correlation  Zeros 

Distinct224
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19138.306
Minimum-17129.6
Maximum634490.92
Zeros261
Zeros (%)53.9%
Negative9
Negative (%)1.9%
Memory size3.9 KiB
2025-03-09T15:53:13.745557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-17129.6
5-th percentile0
Q10
median0
Q311638.612
95-th percentile98634.226
Maximum634490.92
Range651620.52
Interquartile range (IQR)11638.612

Descriptive statistics

Standard deviation59637.584
Coefficient of variation (CV)3.116137
Kurtosis45.511006
Mean19138.306
Median Absolute Deviation (MAD)0
Skewness6.0277483
Sum9262940.3
Variance3.5566414 × 109
MonotonicityNot monotonic
2025-03-09T15:53:13.999960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 261
53.9%
534666.38 1
 
0.2%
634490.916 1
 
0.2%
269901.12 1
 
0.2%
8594.76 1
 
0.2%
3539.376 1
 
0.2%
32645.16 1
 
0.2%
5489.232 1
 
0.2%
187490 1
 
0.2%
497.12 1
 
0.2%
Other values (214) 214
44.2%
ValueCountFrequency (%)
-17129.6 1
 
0.2%
-7505.4 1
 
0.2%
-3670.452 1
 
0.2%
-678.624 1
 
0.2%
-1 1
 
0.2%
-0.4 1
 
0.2%
-0.008 1
 
0.2%
-0.005 1
 
0.2%
-0.001 1
 
0.2%
0 261
53.9%
ValueCountFrequency (%)
634490.916 1
0.2%
534666.38 1
0.2%
435210.66 1
0.2%
382973.219 1
0.2%
370694.26 1
0.2%
282443.036 1
0.2%
269901.12 1
0.2%
213770.3 1
0.2%
192598.986 1
0.2%
190746.216 1
0.2%

MF
Text

Distinct212
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
2025-03-09T15:53:14.447724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length0
Mean length6.4028926
Min length0

Characters and Unicode

Total characters3099
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)43.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
000 5
 
1.3%
00010 4
 
1.0%
00013 4
 
1.0%
783 4
 
1.0%
775 4
 
1.0%
00011 3
 
0.8%
788 3
 
0.8%
289 3
 
0.8%
778 3
 
0.8%
195 2
 
0.5%
Other values (329) 352
91.0%
2025-03-09T15:53:15.021744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
179
 
5.8%
Other values (9) 187
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2902
93.6%
Space Separator 179
 
5.8%
Other Punctuation 11
 
0.4%
Uppercase Letter 6
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 789
27.2%
1 282
 
9.7%
3 275
 
9.5%
4 253
 
8.7%
2 245
 
8.4%
8 241
 
8.3%
7 235
 
8.1%
5 213
 
7.3%
9 200
 
6.9%
6 169
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 2
33.3%
R 1
16.7%
P 1
16.7%
L 1
16.7%
A 1
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 8
72.7%
. 3
 
27.3%
Space Separator
ValueCountFrequency (%)
179
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3093
99.8%
Latin 6
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
179
 
5.8%
Other values (4) 181
 
5.9%
Latin
ValueCountFrequency (%)
M 2
33.3%
R 1
16.7%
P 1
16.7%
L 1
16.7%
A 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
179
 
5.8%
Other values (9) 187
 
6.0%

Observations
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing482
Missing (%)99.6%
Memory size15.7 KiB
2025-03-09T15:53:15.277635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length157
Median length122.5
Mean length122.5
Min length88

Characters and Unicode

Total characters245
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowDOLAR IBAN: TR65 0020 9000 0056 6062 0000 13 EUR IBAN : TR49 0020 9000 0056 6062 0000 10 SWIFT KODU : ZKBATRIS ZIRAAT KATILIM BANKASI / BRANCH: NILÜFER
2nd rowAGENT : LOTE KNITWEAR SWIFT CODE : TCZBTR2A RIB : TR48 0001 0026 9267 7865 3950 03
ValueCountFrequency (%)
6
 
13.0%
iban 2
 
4.3%
0000 2
 
4.3%
0020 2
 
4.3%
0056 2
 
4.3%
9000 2
 
4.3%
swift 2
 
4.3%
6062 2
 
4.3%
dolar 1
 
2.2%
tr65 1
 
2.2%
Other values (24) 24
52.2%
2025-03-09T15:53:15.739003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
16.3%
0 34
 
13.9%
A 13
 
5.3%
T 13
 
5.3%
R 12
 
4.9%
I 12
 
4.9%
6 10
 
4.1%
8
 
3.3%
8
 
3.3%
N 7
 
2.9%
Other values (26) 88
35.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 108
44.1%
Decimal Number 73
29.8%
Space Separator 40
 
16.3%
Control 16
 
6.5%
Other Punctuation 8
 
3.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 13
12.0%
T 13
12.0%
R 12
11.1%
I 12
11.1%
N 7
 
6.5%
B 7
 
6.5%
E 6
 
5.6%
K 5
 
4.6%
S 4
 
3.7%
O 4
 
3.7%
Other values (11) 25
23.1%
Decimal Number
ValueCountFrequency (%)
0 34
46.6%
6 10
 
13.7%
2 7
 
9.6%
9 5
 
6.8%
5 5
 
6.8%
3 3
 
4.1%
1 3
 
4.1%
4 2
 
2.7%
7 2
 
2.7%
8 2
 
2.7%
Control
ValueCountFrequency (%)
8
50.0%
8
50.0%
Other Punctuation
ValueCountFrequency (%)
: 7
87.5%
/ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 137
55.9%
Latin 108
44.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 13
12.0%
T 13
12.0%
R 12
11.1%
I 12
11.1%
N 7
 
6.5%
B 7
 
6.5%
E 6
 
5.6%
K 5
 
4.6%
S 4
 
3.7%
O 4
 
3.7%
Other values (11) 25
23.1%
Common
ValueCountFrequency (%)
40
29.2%
0 34
24.8%
6 10
 
7.3%
8
 
5.8%
8
 
5.8%
: 7
 
5.1%
2 7
 
5.1%
9 5
 
3.6%
5 5
 
3.6%
3 3
 
2.2%
Other values (5) 10
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 244
99.6%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
16.4%
0 34
 
13.9%
A 13
 
5.3%
T 13
 
5.3%
R 12
 
4.9%
I 12
 
4.9%
6 10
 
4.1%
8
 
3.3%
8
 
3.3%
N 7
 
2.9%
Other values (25) 87
35.7%
None
ValueCountFrequency (%)
Ü 1
100.0%

IDDevise
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
1
397 
2
62 
0
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Length

2025-03-09T15:53:15.915962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:16.056761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Most occurring characters

ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 397
82.0%
2 62
 
12.8%
0 25
 
5.2%

Note
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:16.219062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:16.314897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

IDCategorie
Real number (ℝ)

High correlation  Zeros 

Distinct56
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.206612
Minimum0
Maximum74
Zeros73
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:16.488430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q318
95-th percentile50.85
Maximum74
Range74
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.684344
Coefficient of variation (CV)1.187613
Kurtosis2.6626337
Mean13.206612
Median Absolute Deviation (MAD)10
Skewness1.6710979
Sum6392
Variance245.99863
MonotonicityNot monotonic
2025-03-09T15:53:16.742167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 123
25.4%
18 110
22.7%
0 73
15.1%
11 38
 
7.9%
16 13
 
2.7%
7 10
 
2.1%
22 9
 
1.9%
2 6
 
1.2%
14 6
 
1.2%
9 5
 
1.0%
Other values (46) 91
18.8%
ValueCountFrequency (%)
0 73
15.1%
1 123
25.4%
2 6
 
1.2%
4 4
 
0.8%
5 1
 
0.2%
6 4
 
0.8%
7 10
 
2.1%
8 3
 
0.6%
9 5
 
1.0%
10 4
 
0.8%
ValueCountFrequency (%)
74 1
 
0.2%
73 1
 
0.2%
71 1
 
0.2%
69 1
 
0.2%
68 1
 
0.2%
64 2
0.4%
63 1
 
0.2%
61 3
0.6%
60 1
 
0.2%
59 1
 
0.2%

Type
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:16.979378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:17.088762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

FournitMP
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:17.215423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:17.330082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

FournitPF
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
401 
1
83 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

Length

2025-03-09T15:53:17.451967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:17.569180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
82.9%
1 83
 
17.1%

FournitMB
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:17.697894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:17.796228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

NumInterne
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:17.922466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:18.012493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

IDPays
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.89256
Minimum0
Maximum213
Zeros132
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:18.131328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median201
Q3201
95-th percentile201
Maximum213
Range213
Interquartile range (IQR)201

Descriptive statistics

Standard deviation89.372235
Coefficient of variation (CV)0.63432898
Kurtosis-1.1391383
Mean140.89256
Median Absolute Deviation (MAD)0
Skewness-0.90127436
Sum68192
Variance7987.3963
MonotonicityNot monotonic
2025-03-09T15:53:18.316954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
201 297
61.4%
0 132
27.3%
171 21
 
4.3%
103 7
 
1.4%
204 6
 
1.2%
172 5
 
1.0%
18 4
 
0.8%
165 1
 
0.2%
99 1
 
0.2%
203 1
 
0.2%
Other values (9) 9
 
1.9%
ValueCountFrequency (%)
0 132
27.3%
18 4
 
0.8%
35 1
 
0.2%
99 1
 
0.2%
103 7
 
1.4%
107 1
 
0.2%
165 1
 
0.2%
171 21
 
4.3%
172 5
 
1.0%
173 1
 
0.2%
ValueCountFrequency (%)
213 1
 
0.2%
212 1
 
0.2%
211 1
 
0.2%
207 1
 
0.2%
204 6
 
1.2%
203 1
 
0.2%
202 1
 
0.2%
201 297
61.4%
200 1
 
0.2%
173 1
 
0.2%

Pays
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size23.4 KiB
420 
CN
 
18
IN
 
14
TR
 
13
FR
 
6
Other values (8)
 
13

Length

Max length7
Median length0
Mean length0.27479339
Min length0

Characters and Unicode

Total characters133
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.0%

Sample

1st rowTR
2nd rowIN
3rd rowCN
4th rowCN
5th rowTR

Common Values

ValueCountFrequency (%)
420
86.8%
CN 18
 
3.7%
IN 14
 
2.9%
TR 13
 
2.7%
FR 6
 
1.2%
MA 3
 
0.6%
ES 3
 
0.6%
BD 2
 
0.4%
IT 1
 
0.2%
GB 1
 
0.2%
Other values (3) 3
 
0.6%

Length

2025-03-09T15:53:18.512012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn 18
28.1%
in 14
21.9%
tr 13
20.3%
fr 6
 
9.4%
ma 3
 
4.7%
es 3
 
4.7%
bd 2
 
3.1%
it 1
 
1.6%
gb 1
 
1.6%
kr 1
 
1.6%
Other values (2) 2
 
3.1%

Most occurring characters

ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 127
95.5%
Lowercase Letter 6
 
4.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 32
25.2%
R 20
15.7%
C 18
14.2%
I 15
11.8%
T 15
11.8%
F 6
 
4.7%
M 3
 
2.4%
A 3
 
2.4%
E 3
 
2.4%
S 3
 
2.4%
Other values (5) 9
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
u 1
16.7%
n 1
16.7%
s 1
16.7%
e 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 133
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

Echeance
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1818182
Minimum0
Maximum365
Zeros473
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-03-09T15:53:18.662979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.803486
Coefficient of variation (CV)9.0765977
Kurtosis239.49215
Mean2.1818182
Median Absolute Deviation (MAD)0
Skewness14.107405
Sum1056
Variance392.17805
MonotonicityNot monotonic
2025-03-09T15:53:18.809075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 473
97.7%
90 4
 
0.8%
60 3
 
0.6%
365 1
 
0.2%
30 1
 
0.2%
120 1
 
0.2%
1 1
 
0.2%
ValueCountFrequency (%)
0 473
97.7%
1 1
 
0.2%
30 1
 
0.2%
60 3
 
0.6%
90 4
 
0.8%
120 1
 
0.2%
365 1
 
0.2%
ValueCountFrequency (%)
365 1
 
0.2%
120 1
 
0.2%
90 4
 
0.8%
60 3
 
0.6%
30 1
 
0.2%
1 1
 
0.2%
0 473
97.7%

TauxRetenueSource
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
0.0
484 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1452
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 484
100.0%

Length

2025-03-09T15:53:18.994840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:19.108664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
66.7%
Other Punctuation 484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 968
100.0%
Other Punctuation
ValueCountFrequency (%)
. 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

DateExonerationRS
Unsupported

Missing  Rejected  Unsupported 

Missing484
Missing (%)100.0%
Memory size3.9 KiB

ExonerationRS
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:19.252634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:19.361951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

IsPDR
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:19.498279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:19.609057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Timbre
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:19.738127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:19.831877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

IDConditionReglement
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48760331
Minimum-1
Maximum19
Zeros401
Zeros (%)82.9%
Negative38
Negative (%)7.9%
Memory size3.9 KiB
2025-03-09T15:53:19.944225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median0
Q30
95-th percentile4
Maximum19
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2099556
Coefficient of variation (CV)4.5322819
Kurtosis28.590455
Mean0.48760331
Median Absolute Deviation (MAD)0
Skewness4.9220533
Sum236
Variance4.883904
MonotonicityNot monotonic
2025-03-09T15:53:20.121274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 401
82.9%
-1 38
 
7.9%
4 19
 
3.9%
6 6
 
1.2%
2 5
 
1.0%
11 5
 
1.0%
3 3
 
0.6%
7 2
 
0.4%
16 1
 
0.2%
17 1
 
0.2%
Other values (3) 3
 
0.6%
ValueCountFrequency (%)
-1 38
 
7.9%
0 401
82.9%
2 5
 
1.0%
3 3
 
0.6%
4 19
 
3.9%
6 6
 
1.2%
7 2
 
0.4%
8 1
 
0.2%
11 5
 
1.0%
14 1
 
0.2%
ValueCountFrequency (%)
19 1
 
0.2%
17 1
 
0.2%
16 1
 
0.2%
14 1
 
0.2%
11 5
 
1.0%
8 1
 
0.2%
7 2
 
0.4%
6 6
 
1.2%
4 19
3.9%
3 3
 
0.6%

IDModeReglement
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
413 
1
58 
2
 
8
6
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Length

2025-03-09T15:53:20.330834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:20.475111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 413
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

ToleranceMAxAccepte
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
0.0
484 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1452
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 484
100.0%

Length

2025-03-09T15:53:20.660761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:20.750946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
66.7%
Other Punctuation 484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 968
100.0%
Other Punctuation
ValueCountFrequency (%)
. 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

IDBanque
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:20.934068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:21.024440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

AdresseBanque
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:21.135773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:21.228569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VilleBanque
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:21.339331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:21.437080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumCompte
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:21.545875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:21.635693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CodeSwift
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:21.742363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:21.832319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IBAN
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:21.948747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:22.045678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NonAssujettiTVA
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:22.173133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:22.275502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

DelaisLivraison
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:22.421272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:22.532338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Reference
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:22.667540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:22.786701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IDFournisseurParent
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:22.896566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:23.003379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Difference
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
0.0
484 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1452
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 484
100.0%

Length

2025-03-09T15:53:23.126694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:23.231470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
66.7%
Other Punctuation 484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 968
100.0%
Other Punctuation
ValueCountFrequency (%)
. 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1452
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 968
66.7%
. 484
33.3%

AppliqueFodec
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:23.359583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:23.470210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Etat
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
1
405 
0
79 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Length

2025-03-09T15:53:23.593493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:23.710467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Most occurring characters

ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 405
83.7%
0 79
 
16.3%

Login_FRS
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
484 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
484
100.0%

Length

2025-03-09T15:53:23.835250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:23.924314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IDCGAFournisseur
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
462 
2
 
18
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

Length

2025-03-09T15:53:24.033777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:24.141873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 462
95.5%
2 18
 
3.7%
1 4
 
0.8%

IsMP
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
483 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

Length

2025-03-09T15:53:24.278466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:24.390614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 483
99.8%
1 1
 
0.2%

IsPF
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
255 
1
229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

Length

2025-03-09T15:53:24.522822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:24.634605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

Most occurring characters

ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255
52.7%
1 229
47.3%

isService
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
1
318 
0
166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

Length

2025-03-09T15:53:24.778487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:24.888046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

Most occurring characters

ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 318
65.7%
0 166
34.3%

CodeTVA
Text

Distinct247
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
2025-03-09T15:53:25.159386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.1880165
Min length0

Characters and Unicode

Total characters3479
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)49.8%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
fr 52
 
12.7%
fr42424064707 5
 
1.2%
778 4
 
1.0%
fr79410034607 3
 
0.7%
fr17784364150 3
 
0.7%
000 2
 
0.5%
23 2
 
0.5%
950 2
 
0.5%
fr02 2
 
0.5%
796 2
 
0.5%
Other values (326) 331
81.1%
2025-03-09T15:53:25.954298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 318
9.1%
7 305
8.8%
2 302
8.7%
8 300
8.6%
3 299
8.6%
0 288
8.3%
1 267
7.7%
5 259
7.4%
F 251
 
7.2%
R 251
 
7.2%
Other values (9) 639
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2812
80.8%
Uppercase Letter 508
 
14.6%
Space Separator 156
 
4.5%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 318
11.3%
7 305
10.8%
2 302
10.7%
8 300
10.7%
3 299
10.6%
0 288
10.2%
1 267
9.5%
5 259
9.2%
9 248
8.8%
6 226
8.0%
Uppercase Letter
ValueCountFrequency (%)
F 251
49.4%
R 251
49.4%
B 2
 
0.4%
G 2
 
0.4%
V 1
 
0.2%
D 1
 
0.2%
Space Separator
ValueCountFrequency (%)
156
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2971
85.4%
Latin 508
 
14.6%

Most frequent character per script

Common
ValueCountFrequency (%)
4 318
10.7%
7 305
10.3%
2 302
10.2%
8 300
10.1%
3 299
10.1%
0 288
9.7%
1 267
9.0%
5 259
8.7%
9 248
8.3%
6 226
7.6%
Other values (3) 159
5.4%
Latin
ValueCountFrequency (%)
F 251
49.4%
R 251
49.4%
B 2
 
0.4%
G 2
 
0.4%
V 1
 
0.2%
D 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 318
9.1%
7 305
8.8%
2 302
8.7%
8 300
8.6%
3 299
8.6%
0 288
8.3%
1 267
7.7%
5 259
7.4%
F 251
 
7.2%
R 251
 
7.2%
Other values (9) 639
18.4%

IDPlanComptable
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters484
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484
100.0%

Length

2025-03-09T15:53:26.154593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:53:26.268648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 484
100.0%

Most occurring characters

ValueCountFrequency (%)
0 484
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
100.0%

Interactions

2025-03-09T15:53:02.511423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:51.872723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:53.417005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:55.005504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:56.616073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.170366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:59.493341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.988416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:02.719532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:52.058288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:53.619056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:55.191333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:56.811711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.359519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:59.719784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:01.200536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:02.972967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:52.250943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:53.811529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:55.409529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.000342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.571285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:59.925136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:01.487565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:03.177919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:52.483772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:54.032213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:55.623011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.206165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.698277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.125278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:01.750503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:03.386434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:52.683496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:54.221624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:55.808748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.413666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.831503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.315634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:01.916315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:03.603071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:52.883261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:54.409919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:56.021479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.594014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:58.933805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.481762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:02.189903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:03.794444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:53.060276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:54.587082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:56.223230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.743232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:59.087139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.627636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:02.301727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:03.999750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:53.223580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:54.799489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:56.434814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:57.919896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:59.283764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:00.793597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:53:02.420796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-09T15:53:26.386203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ChiffreEcheanceEtatFournitPFIDCGAFournisseurIDCategorieIDConditionReglementIDDeviseIDFournisseurIDModeReglementIDPaysIsMPIsPFPaysReglementsSoldeisService
Chiffre1.000-0.1970.0610.1520.3800.353-0.0510.0000.2340.0000.4890.0000.1630.2460.8180.7350.176
Echeance-0.1971.0000.0000.0710.386-0.1390.3580.0000.0160.386-0.1300.0000.1130.477-0.166-0.1220.168
Etat0.0610.0001.0000.9040.0000.4630.4010.7150.8880.7650.6540.0000.4250.7870.0340.0700.604
FournitPF0.1520.0710.9041.0000.1930.4780.4570.7100.9620.8400.6220.0000.4290.8440.1150.1560.588
IDCGAFournisseur0.3800.3860.0000.1931.0000.0990.3850.0370.3130.3410.5600.0000.1140.4260.3740.3760.187
IDCategorie0.353-0.1390.4630.4780.0991.000-0.0410.3350.1300.1810.7300.0000.3270.0000.6090.0670.768
IDConditionReglement-0.0510.3580.4010.4570.385-0.0411.0000.2440.1650.522-0.0010.0000.2830.335-0.037-0.0440.394
IDDevise0.0000.0000.7150.7100.0370.3350.2441.0000.6320.4810.4950.0000.4300.5050.0000.0000.634
IDFournisseur0.2340.0160.8880.9620.3130.1300.1650.6321.0000.5010.2040.1450.8310.3620.0330.1940.721
IDModeReglement0.0000.3860.7650.8400.3410.1810.5220.4810.5011.0000.4570.0000.3930.6040.0000.0000.530
IDPays0.489-0.1300.6540.6220.5600.730-0.0010.4950.2040.4571.0000.0000.3110.3030.7370.1570.956
IsMP0.0000.0000.0000.0000.0000.0000.0000.0000.1450.0000.0001.0000.0000.0000.0000.0000.000
IsPF0.1630.1130.4250.4290.1140.3270.2830.4300.8310.3930.3110.0001.0000.3510.1180.2010.293
Pays0.2460.4770.7870.8440.4260.0000.3350.5050.3620.6040.3030.0000.3511.0000.2450.2000.485
Reglements0.818-0.1660.0340.1150.3740.609-0.0370.0000.0330.0000.7370.0000.1180.2451.0000.4130.143
Solde0.735-0.1220.0700.1560.3760.067-0.0440.0000.1940.0000.1570.0000.2010.2000.4131.0000.202
isService0.1760.1680.6040.5880.1870.7680.3940.6340.7210.5300.9560.0000.2930.4850.1430.2021.000

Missing values

2025-03-09T15:53:04.518306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T15:53:05.209648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDFournisseurFournisseurAdresseCodeEmailTelFaxisFournisseurChiffreReglementsSoldeMFObservationsIDDeviseNoteIDCategorieTypeFournitMPFournitPFFournitMBNumInterneIDPaysPaysEcheanceTauxRetenueSourceDateExonerationRSExonerationRSIsPDRTimbreIDConditionReglementIDModeReglementToleranceMAxAccepteIDBanqueAdresseBanqueVilleBanqueNumCompteCodeSwiftIBANNonAssujettiTVADelaisLivraisonReferenceIDFournisseurParentDifferenceAppliqueFodecEtatLogin_FRSIDCGAFournisseurIsMPIsPFisServiceCodeTVAIDPlanComptable
06BALINLER PAZARLAMA VE TICARETGULTEPE MAH TURE SOK N°12FRM0061500.00.00.0None201001000TR00.0None000210.000000.00000100
119DRISHTI APPARELSPLOT NO 180 SECTOR -6 IM MANESFRM0107100.00.00.0None201001000IN00.0None000-110.000000.00000100
225TRUMODE INTERNATIONAL LTD.3-5F N0 9. LOFT POWER N0 28 XIFRM0141000.00.00.0None201001000CN00.0None000-110.000000.00000100
329SHANGHAI SILK GROUP TRADING DEVELOPMENT CO., LTDN°283 WUXING ROADFRM0160300.00.00.0None201001000CN00.0None000-110.000000.00000100
432GULEKS TEKSTIL SAN TIC LTD STIAMBARLI PETROL OFSI DOLUMGULEKS TEKSTIL00.00.00.0None10100100171TR00.0None000-110.000000.00110100
533AIT APPAREL CO LTD3RD FLOOR BLOCK 8FRM0182400.00.00.0None201001000CN00.0None000-110.000000.00000100
638ANTIK DIS TICARET LTD STIBEYSAN SANAYRI SITESI DEREBOYUFRM0221400.00.00.0None201001000TR00.0None000-110.000000.00000100
739DEMKA TEKSTIL SAN DIS TIC ASKARAYOLLARI MAH 564-1 SOK N°1FRM0223200.00.00.0None101001000TR00.0None000-110.000000.00000100
842DYNAMIC DESIGN INC.PLOT NO 417 PACE CITY IIFRM0230300.00.00.0None201001000IN00.0None000410.000000.00000100
946TIANJIN PUNLEET TRADING CO LTDRM 902 A03 NO 17 CENTURY BLDGFRM0243500.00.00.0None201001000CN00.0None000-110.000000.00000100
IDFournisseurFournisseurAdresseCodeEmailTelFaxisFournisseurChiffreReglementsSoldeMFObservationsIDDeviseNoteIDCategorieTypeFournitMPFournitPFFournitMBNumInterneIDPaysPaysEcheanceTauxRetenueSourceDateExonerationRSExonerationRSIsPDRTimbreIDConditionReglementIDModeReglementToleranceMAxAccepteIDBanqueAdresseBanqueVilleBanqueNumCompteCodeSwiftIBANNonAssujettiTVADelaisLivraisonReferenceIDFournisseurParentDifferenceAppliqueFodecEtatLogin_FRSIDCGAFournisseurIsMPIsPFisServiceCodeTVAIDPlanComptable
474704NOVAGRAAF2 Rue Sarah Bernhardt\r\nCS 90017\r\n92665 ASNIERES-SUR-SEINEF0000135tm.fr@novagraaf.com+33(0)1 49 64 60 0001562.41562.40.063200155800071None10740000020100.0None000000.000000.0010001FR 06 632 001 5580
475705NONE MANAGEMENT GROUP12 ALLEE DES EGLANTINES 92260 FONTENAY-AUX-ROSESF0000136elie.zinsou@gmail.com90818641400011041400.018600.022800.090818641400011None10140000020100.0None000000.000000.0010001FR719081864140
476706ONATEKSNECIP FAZIL KISAKUREK MH. GAZI CD. NO:78 ESENYURT ISTANBUL010yasin.basoglu@onat.com.tr+902126894242+90212689081600.00.00.0None10100000171900.0None000230.000000.00120100
477707UTG ISTANBUL GIYIM SAN. A.S.NAMIK KEMAL MAH. 120. SOK. NO:16/1 K:3 ESENYURT ISTANBUL012muhsin.arkon@utgistanbul.com.tr+90 533479784700.00.00.0None10100000171900.0None000230.000000.00100100
478708NAHULI GUANGZHOU FASHION COMPANY LTDB1409,14TH FLOOR,CHINA PLAZA CHINA INT CENTRE BUILDING,NO.33 ZHONGSHAN 3 ROAD,GUANGZHOU013sunshine@nahuli.com86 1380977471800.00.00.0None1010000010300.0None0001100.000000.00100100
479709SHINY STARS (HONGKONG) TRADING COMPANY LIMITEDUNIT A7 12/F ASTORIA BUILDING 34 ASHLEY ROAD TSIM SHA TSUI KL014sunshine@nahuli.com86 1380977471800.00.00.0None1010000010300.0None0001100.000000.00100100
480710Zhuo Silun Wool Weaving FactoryNo. 5 501, Lin Cuo Industrial Zone, Jinlin Road, Lin Cuo Village, Outer Sand Street, Longhu district City\r\nShantou City015tim@classysweaterfactory.com86-188 2605 217500.00.00.0None2010000010300.0None0001100.000000.00100100
481711SAFI GROUP MAKINA GIDA SANAYI TICARET ITHALAT IHRA15 Temmuz Mah. 1506 sok. Bina No:7 No:31 BAGCILAR/ISTANBUL016ismail@safitekstil.com.tr0 551 408 54 0800.00.00.0None1010000017100.0None0001900.000000.00100100
482712ZHEJIANG TRIMAX INTERNATIONAL GROUPBUILDING A AREA NO.900 HUZHI ROAD,WUXING DISTRICT, HUZHOU CITY, ZHEJIANG PROVINCE,CHINA017LOIS@TRIMAX.CC0086-572-262608700.00.00.0None2010000010300.0None0001100.000000.00100100
483713HK NEW WORLD FASHION IM & EX LIMITEDA-612, Changsheng Cloth Building, No.363 Shicha Road, Baiyun District, 510430, Guangzhou CHINA018cihan@hknewworldfashiongroup.com+86 - 1591437893400.00.00.0None2000000010300.0None0001100.000000.00100100